Illinois Data Bank - Dataset

Data for: Learned 1-D passive scalar advection to accelerate chemical transport modeling: a case study with GEOS-FP horizontal wind fields
Cite this dataset:
Park, Manho; Zheng, Zhonghua; Riemer, Nicole; Tessum, Christopher (2024): Data for: Learned 1-D passive scalar advection to accelerate chemical transport modeling: a case study with GEOS-FP horizontal wind fields. University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-4743181_V1
Dataset Description

This dataset contains the training results (model parameters, outputs), datasets for generalization testing, and 2-D implementation used in the article "Learned 1-D passive scalar advection to accelerate chemical transport modeling: a case study with GEOS-FP horizontal wind fields." The article will be submitted to Artificial Intelligence for Earth Systems. The datasets are saved as CSV for 1-D time-series data and *netCDF for 2-D time series dataset. The model parameters are saved in every training epoch tested in the study.

Subject Technology and Engineering
Keywords Air quality modeling; Coarse-graining; GEOS-Chem; Numerical advection; Physics-informed machine learning; Transport operator
License CC0
Funder U.S. National Aeronautics and Space Administration (NASA)-Grant:80NSSC21K1813
Funder U.S. Environmental Protection Agency-Grant:RD-84001201-0
Corresponding Creator Christopher Tessum
Downloaded 102 times
Version DOI Comment Publication Date
1 10.13012/B2IDB-4743181_V1 2024-05-23

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RelatedMaterial update: {"material_type"=>["Pre-print", "Preprint"], "note"=>[nil, ""]} 2024-05-23T18:02:49Z